Economic Botany

, Volume 66, Issue 4, pp 383–397 | Cite as

Impact of Future Climate and Land Use Change on Non-timber Forest Product Provision in Benin, West Africa: Linking Niche-based Modeling with Ecosystem Service Values

  • Jonathan Heubes
  • Katja Heubach
  • Marco Schmidt
  • Rüdiger Wittig
  • Georg Zizka
  • Ernst-August Nuppenau
  • Karen Hahn

Impact of Future Climate and Land Use Change on Non-timber Forest Product Provision in Benin, West Africa: Linking Niche-based Modeling with Ecosystem Service Values. Non-timber forest products (NTFPs) make a major contribution to the livelihoods of the West African population. However, these ecosystem services are threatened by climate and land use change. Our study aims at 1) the quantification and monetary mapping of important NTFPs, and 2) developing a novel approach to assess the impacts of climate and land use change on the economic benefits derived from these NTFPs. We performed household interviews in northern Benin to gather data on annual quantities of collected NTFPs from the three most important savanna tree species: Adansonia digitata, Parkia biglobosa, and Vitellaria paradoxa. Current market prices of the NTFPs were derived from local markets. We assessed the species’ current and future (2050) occurrence probabilities by calibrating niche-based models with climate and land use data at a 0.1° resolution (cell: ~10 × 10 km). To assess future economic gains and losses, respectively, we linked modeled species’ occurrence probabilities with the spatially assigned monetary values. Highest current annual benefits are obtained from V. paradoxa (USD 54,111 ± 28,126/cell), followed by P. biglobosa (USD 32,246 ± 16,526/cell) and A. digitata (USD 9,514 ± 6,243/cell). The future simulations showed spatially varying impacts of environmental change. In particular A. digitata might benefit in some regions. However, large areas are projected to lose up to 50 % of their current economic value by 2050 with regard to the three species. Our findings provide a first benchmark for local policy-makers to economically compare different land use options and adjust existing management strategies.

Key Words

Benin climate envelope models ecosystem service mapping species distribution modeling rural livelihoods 

L’impact du changement climatique et l’aménagement du territoire sur les produits forestiers non ligneux en Benin, Afrique de l’Ouest : Combiner niche-based modeling aux valeurs des services écosystémiques. Les produits forestiers non ligneux (NTFPs) apportent une contribution essentielle au niveau des moyens de subsistance des populations de l’Afrique de l’Ouest. Toutefois, ces services écosystémiques sont menacés par le changement climatique et l’aménagement du territoire. Notre étude vise 1) la quantification et la cartographie monétaire des NTFPs importants et 2) à développer une approche novatrice pour évaluer l’impact du changement climatique et l’aménagement du territoire sur les bénéfices économiques dérivés des NTFPs. Notre étude se base sur des données collectées dans des enquêtes réalisées dans des ménages ruraux au nord du Bénin et visant à analyser les quantités annuelles des NTFPs récoltés, et ceci pour les trois espèces d’arbres de savanes les plus importantes: Vitellaria paradoxa, Parkia biglobosa and Adansonia digitata. Les prix du marché courants des NTFPs étaient déduits des marchés locaux. Nous avons évalué les probabilités d’occurrence actuelles et futures (2050) des espèces en calibrant les modèles niche-basés avec les données climatiques et sur l’utilisation des terres, en travaillant avec une résolution de 0.1° (cellule: ~10 × 10 km). Pour évaluer les gains respectivement les pertes économiques futures, nous avons combiné les probabilités d’occurrence des espèces avec leur valeur monétaire attribuée, calculée sur l’espace. Les bénéfices économiques annuels le plus importants sont réalisés avec V. paradoxa (USD 54,111 ± 28,126/cellule), suivi de P. biglobosa (USD 32,246 ± 16,526/cellule) et A. digitata (USD 9,514 ± 6,243/cellule). Les futures simulations ont montré que les impacts de changement environnemental vont être spatialement variés. En particulier le baobab peut profiter dans quelques régions. Cependant, selon les pronostiques des vastes zones auront perdu jusqu’à 50 pour cent de leur valeur économique en 2050 pour les trois espèces. Nos résultats mettent a la disposition des décideurs locaux un indice de référence pour permettre une comparaison des diverses options d’utilisation des terres selon des critères économiques et ensuite ajuster les stratégies de gestion existantes, dans le but de parvenir à une utilisation des terres écologiquement et économiquement durable.



The present study was conducted at the Biodiversity and Climate Research Centre (BiK-F), Frankfurt am Main, Germany, and funded by the research funding program “LOEWE–Landes–Offensive zur Entwicklung Wissenschaftlich–Ökonomischer Exzellenz” of Hesse’s Ministry of Higher Education, Research, and the Arts. The authors are grateful to Gnanando Saidou, Laurent Akissatom, and Etienne Dossou who assisted in field work, and especially all respondents who took part in the surveys. We thank Marion Mehring for valuable comments on the manuscript. Sincere thanks go further to Professor Brice Sinsin (University of Abomey-Calavi, Benin) for scientific and logistic support in Benin. Furthermore, we would like to thank two anonymous reviewers for their constructive comments on this work.

Literature Cited

  1. Adomou, A. C. 2005. Vegetation patterns and environmental gradients in Benin: Implications for biogeography and conservation. Ph.D. thesis, Wageningen University, Wageningen. ISBN 90-8504-308-5.Google Scholar
  2. Agbahungba, G. and D. Depommier. 1989. Aspects du parc à karité-nérés (Vitellaria paradoxa Gaertn. f., Parkia biglobosa Jacq. Benth.) dans le sud du Borgou (Benin). Bois et Forêts des Tropiques 222:41–54.Google Scholar
  3. Angelsen, A. and S. Wunder. 2003. Exploring the forest-poverty link: Key concepts, issues and research implications. CIFOR, Bogor, Indonesia.Google Scholar
  4. Araújo, M. B., R. J. Whittaker, R. J. Ladle, and M. Erhard. 2005. Reducing uncertainty in projections of extinction risk from climate change. Global Ecology and Biogeography 14:529–38.CrossRefGoogle Scholar
  5. Babulo, B., B. Muys, F. Nega, E. Tollens, J. Nyssen, J. Deckers, and E. Mathijs. 2009. The economic contribution of forest resource use to rural livelihoods in Tigray, Northern Ethiopia. Forest Policy and Economics 11:109–117.CrossRefGoogle Scholar
  6. Baddeley, A. and R. Turner. 2005. Spatstat: An R package for analyzing spatial point patterns. Journal of Statistical Software 12:1–42. Scholar
  7. Besco, E., E. Braccioli, S. Vertuani, P. Ziosi, F. Brazzo, R. Bruni, G. Sacchetti, and S. Manfredini. 2007. The use of photochemiluminescence for the measurement of the integral antioxidant capacity of baobab products. Food Chemistry 102:1352–1356.CrossRefGoogle Scholar
  8. Boffa, J. M. 1999. Agroforestry parklands in sub-Saharan Africa. Food and Agriculture Organization of the United Nations (FAO), Rome.Google Scholar
  9. Business Times. 2010. Bright light shines over Ghana’s shea-nut industry. Accra, Ghana: Business Times Magazine Africa.
  10. Cavendish, W. 2002. Quantitative methods for estimating the economic value of resource use to rural households. Pages 17–65 in B. M. Campbell and M. K. Luckert, eds., Uncovering the hidden harvest: Valuation methods for woodland and forest resources. Earthscan Publications, Ltd, London.Google Scholar
  11. Chatelain, C., L. Aké Assi, L. Gautier, and R. Spichiger. 2011. Atlas des plantes de Côte d’Ivoire. Boissiera 64:1–250.Google Scholar
  12. Chen, N., H. Li, and L. Wang. 2009. A GIS-based approach for mapping direct use value of ecosystem services at a county scale: Management implications. Ecological Economics 68:2768–2776.CrossRefGoogle Scholar
  13. Clarke, J., W. Cavendish, and C. Coote. 1996. Rural households and Miombo woodlands: Use, values and management. Pages 101–135 in B. Campbell, ed., The Miombo in transition: Woodlands and welfare in Africa. Center for International Forestry Research (CIFOR), Bogor, Bogor, Indonesia.Google Scholar
  14. Costanza, R., R. d’Arge, R. de Groot, S. Farber, M. Grasso, B. Hannen, K. Limburg, S. Naeem, R. O’Neill, J. Paruelo, R. G. Raskin, P. Sutton, and M. van den Belt. 1997. The value of the world’s ecosystem services and natural capital. Nature 387:252–261.CrossRefGoogle Scholar
  15. ———, M. Wilson, A. Troy, A. Voinov, S. Liu, and J. D’Agostino. 2006. The value of New Jersey’s ecosystem services and natural capital. Gund Institute for Ecological Economics, University of Vermont, Burlington.Google Scholar
  16. Cunningham, A. B. 2001. Applied ethnobotany—People, wild plant use and conservation. Earthscan Publications, Ltd., London.Google Scholar
  17. de Berg, M., O. Cheong, M. van Kreveld, and M. Overmars. 2008. Computational geometry: Algorithms and applications, 3rd edition. Springer, Berlin, Heidelberg, New York.Google Scholar
  18. de Merode, E., K. Homewood, and G. Cowlishaw. 2004. The value of bushmeat and other wild foods to rural households living in extreme poverty in Democratic Republic of Congo. Biological Conservation 118:573–581.CrossRefGoogle Scholar
  19. Dormann, C. F. 2007. Promising the future? Global change projections of species distributions. Basic and Applied Ecology 8:387–397.CrossRefGoogle Scholar
  20. Egoh, B., B. Reyers, M. Rouget, D. M. Richardson, D. C. Le Maitre, and A. S. van Jaarsveld. 2008. Mapping ecosystem services for planning and management. Agriculture Ecosystems and Environment 127:135–140.CrossRefGoogle Scholar
  21. Eigenbrod, F., P. R. Armsworth, B. J. Anderson, A. Heinemeyer, S. Gillings, D. B. Roy, C. D. Thomas, and K. J. Gaston. 2010. The impact of proxy-based methods on mapping the distribution of ecosystem services. Journal of Applied Ecology 47:377–385.CrossRefGoogle Scholar
  22. FAO. 2010a. Crop prospects and food situation. Crop Prospects and Food Situation, No. 2, May 2010, Global Information and Early Warning System. Rome: FAO.
  23. ———. 2010b. Global forest resources assessment. FAO Forestry Paper 163, Rome.Google Scholar
  24. Faye, M. D., J. C. Weber, B. Mounkoro, and J.-M. Dakouo. 2010. Contribution of parkland trees to farmers’ livelihoods: A case study from Mali. Development in Practice 20:428–434.CrossRefGoogle Scholar
  25. Fielding, A. H. and J. F. Bell. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24:38–49.CrossRefGoogle Scholar
  26. Gebauer, J., K. El-Siddig, and G. Ebert. 2002. Baobab (Adansonia digitata L.): A review on a multipurpose tree with promising future in the Sudan. Gartenbauwissenschaft 67:155–160.Google Scholar
  27. Glèlè Kakaï, R., T. J. D. Akpona, A. E. Assogbadjo, O. G. Gaoué, S. Chakeredza, P. C. Gnanglè, G. A. Mensah, and B. Sinsin. 2011. Ecological adaptation of the shea butter tree (Vitellaria paradoxa C.F. Gaertn.) along climatic gradient in Bénin, West Africa. African Journal of Ecology 49:440–449.CrossRefGoogle Scholar
  28. Godoy, R., D. Wilkie, H. Overman, A. Cubas, G. Cubas, J. Demmer, K. McSweeney, and N. Brokaw. 2000. Valuation of consumption and sale of forest goods from a Central American rain forest. Nature 406:62–63.PubMedCrossRefGoogle Scholar
  29. Goklany, I. M. 2009. Discounting the future. Regulation: 32:36–40.Google Scholar
  30. Gram, S. 2001. Economic valuation of special forest products: An assessment of methodological shortcomings. Ecological Economics 36:109–117.CrossRefGoogle Scholar
  31. Grünwald, J. and M. Galizia. 2005. Market brief in the European Union for selected natural ingredients derived from native species: Adansonis digitata L. Baobab. The United Nations Conference on Trade and Development (UNCTAD), BioTrade Initiative/BioTrade Facilitation Programme (BTFP), GenevaGoogle Scholar
  32. Guisan, A. and N. E. Zimmermann. 2000. Predicitive habitat distribution models in ecology. Ecological Modelling 135:147–186.CrossRefGoogle Scholar
  33. Heubach, K. Forthcoming. Social differentiation as an important source for improving conservation measures: The impact of ethnic affiliation on the valuation of NTFP-providing woody species in northern Benin, West Africa.Google Scholar
  34. ———, R. Wittig, E.-A. Nuppenau, and K. Hahn. 2011. The economic importance of non-timber forest products (NTFPs) for livelihood maintenance of rural West African communities: A case study from northern Benin. Ecological Economics 70:1991–2001.CrossRefGoogle Scholar
  35. Heubes, J., I. Kühn, K. König, R. Wittig, G. Zizka, and K. Hahn. 2011. Modelling biome shifts and tree cover change for 2050 in West Africa. Journal of Biogeography 39:2248–2258.CrossRefGoogle Scholar
  36. Hijmans, R. J., S. E. Cameron, J. L. Parra, P. G. Jones, and A. Jarvis. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25:1965–1978.CrossRefGoogle Scholar
  37. INSAE. 2000. Evolution des filières d’exploration au Benin: Cas de quatre produits. Institut National de la Statistique et de l’Analyse Economique, Cotonou, Benin.Google Scholar
  38. ——— 2008. Tableau de bord social 2008. Profils socio-économiques et indicateurs de développement. Institut National de la Statistique et de l’Analyse Economique, Cotonou, Benin.Google Scholar
  39. IPCC. 2007. Climate change 2007: The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate. Change, Cambridge.Google Scholar
  40. Janßen, T., M. Schmidt, S. Dressler, K. Hahn-Hadjali, M. Hien, S. Konaté, A. M. Lykke, A. Mahamane, B. Sambou, B. Sinsin, A. Thiombiano, R. Wittig, and G. Zizka. 2011. Addressing data property rights concerns and providing incentives for collaborative data pooling: The West African vegetation database approach. Journal of Vegetation Science 22:614–620.CrossRefGoogle Scholar
  41. Kamanga, P., P. Vedeld, and E. Sjaastad. 2009. Forest incomes and rural livelihoods in Chiradzulu District, Malawi. Ecological Economics 68:613–624.CrossRefGoogle Scholar
  42. Lombard, C. 2008. A GRAS notification for use as a food ingredient in fruit bars and fruit smoothies. PhytoTrade Africa, Southern African Natural Products Trade Association (SANPROTA).Google Scholar
  43. MA. 2005. Millennium Ecosystem Assessment, 2005. Ecosystems and human well-being: synthesis. Island Press, Washington, D.C.Google Scholar
  44. Maranz, S., W. Kpikpi, Z. Wiesman, A. De Saint Sauveur, and B. Chapagain. 2004. Nutritional values and indigenous preferences for shea fruits (Vitellaria paradoxa C.F. Gaertn. F.) in African agroforestry parklands. Economic Botany 58:588–600.CrossRefGoogle Scholar
  45. Naidoo, R., A. Balmford, R. Costanza, B. Fisher, R. E. Green, B. Lehner, T. R. Malcolm, and T. H. Ricketts. 2008. Global mapping of ecosystem services and conservation priorities. Proceedings of the National Academy of Sciences 105:9495–9500.CrossRefGoogle Scholar
  46. Norris, K., A. Asase, B. Collen, J. Gockowski, J. Mason, B. Phalan, and A. Wade. 2010. Biodiversity in a forest-agriculture mosaic—The changing face of West African rainforests. Biological Conservation 143(10):2341–2350.CrossRefGoogle Scholar
  47. OECD. 2010. OECD-FAO Agricultural Outlook 2010–2019. Organisation for Economic Co-operation and Development.Google Scholar
  48. Plummer, M. L. 2009. Assessing benefit transfer for the valuation of ecosystem services. Frontiers in Ecology and the Environment 7:38–45.CrossRefGoogle Scholar
  49. ProCGRN. 2011. Stratégie ProCGRN 3. Filière karité—Changements structurels durables visés (Institutions, Services, OPA, etc.). ProCGRN, GTZ, Natitingou, Benin.Google Scholar
  50. R Development Core Team. 2011. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
  51. Ramirez, J. and A. Jarvis. 2008. High resolution statistically downscaled future climate surfaces. CIAT (International Centre for Tropical Agriculture), Cali, Colombia.Google Scholar
  52. Rothman, D. S., J. Agard, and J. Alcamo. 2007. Chapter 9: The future today. Pages 395–454 in M. Schomaker, M. Keating, and M. Chenje, eds., Global Environmental Outlook 4 (GEO-4) environment for development. United Nations Environment Programme (UNEP), Valletta, Malta.Google Scholar
  53. Sala, O. E., F. S. C., III Chapin, J. J. Armesto, E. Berlow, J. Bloomfield, R. Dirzo, E. Huber-Sanwald, L. F. Huenneke, R. B. Jackson, A. Kinzig, R. Leemans, D. M. Lodge, H. A. Mooney, M. Oesterheld, N. L. Poff, M. T. Sykes, B. H. Walker, M. Walker, and D. Wall. 2000. Global biodiversity scenarios for the year 2100. Science 287:1770–1774.Google Scholar
  54. Schaldach, R., J. Alcamo, L. Koch, C. Kölking, D. M. Lapola, J. Schüngel, and J. Priess. 2011. An integrated approach to modelling land-use change on continental and global scales. Environmental Modelling and Software 26:1041–1051.CrossRefGoogle Scholar
  55. Scheiter, S. and S. I. Higgins. 2009. Impacts of climate change on the vegetation of Africa: An adaptive dynamic vegetation modelling approach. Global Change Biology 15:2224–2246.CrossRefGoogle Scholar
  56. Scholes, R. J. 1997. Savanna. Cambridge University Press, Cambridge.Google Scholar
  57. Schreckenberg, K. 1996. Forests, fields and markets: A study of indigenous tree products in the woody savannas of the Bassila region, Benin. University of London, London.Google Scholar
  58. Schröter, D., W. Cramer, R. Leemans, I. C. Prentice, M. B. Araújo, N. W. Arnell, A. Bondeau, H. Bugmann, T. R. Carter, C. A. Gracia, A. C. de la Vega-Leinert, M. Erhard, F. Ewert, M. Glendining, J. I. House, S. Kankaanpää, R. T. J. Klein, S. Lavorel, M. Lindner, M. J. Metzger, J. Meyer, T. D. Mitchell, I. Reginster, M. Rounsevell, S. Sabaté, S. Sitch, B. Smith, J. Smith, P. Smith, M. T. Sykes, K. Thonicke, W. Thuiller, G. Tuck, S. Zaehle, and B. Zierl. 2005. Ecosystem service supply and vulnerability to global change in Europe. Science 310:1334–1337.CrossRefGoogle Scholar
  59. Schumann, K., R. Wittig, A. Thiombiano, U. Becker, and K. Hahn. 2010. Impact of land-use type and bark- and leaf-harvesting on population structure and fruit production of the baobab tree (Adansonia digitata L.) in a semi-arid savanna, West Africa. Forest Ecology and Management 260:2035–2044.CrossRefGoogle Scholar
  60. Shackleton, C. M., S. E. Shackleton, E. Buiten, and N. Bird. 2007. The importance of dry woodlands and forests in rural livelihoods and poverty alleviation in South Africa. Forest Policy and Economics 9:558–577.CrossRefGoogle Scholar
  61. Shone, B. M. and J. L. Caviglia-Harris. 2006. Quantifying and comparing the value of non-timber forest products in the Amazon. Ecological Economics 58:249–267.CrossRefGoogle Scholar
  62. Sidibé, M. and J. T. Williams. 2002. Baobab. Adansonia digitata. International Centre for Underutilised Crops, Southampton, England.Google Scholar
  63. Sitch, S., B. Smith, I. C. Prentice, A. Arneth, A. Bondeau, W. Cramer, J. O. Kaplan, S. Levis, W. Lucht, M. T. Sykes, K. Thonicke, and S. Venevsky. 2003. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology 9:161–185.CrossRefGoogle Scholar
  64. Swets, J. A. 1988. Measuring the accuracy of diagnostic systems. Science 240:1285–1293.PubMedCrossRefGoogle Scholar
  65. TEEB. 2008. The economics of ecosystems and biodiversity: An interim report. European Commission, Brussels. Scholar
  66. Teklehaimanot, Z. 2004. Exploiting the potential of indigenous agroforestry trees: Parkia biglobosa and Vitellaria paradoxa in sub-Saharan Africa. Agroforestry Systems 61(2):207–220.CrossRefGoogle Scholar
  67. The Commission of the European Communities. 2008. COMMISSION DECISION of 27 June 2008 authorising the placing on the market of Baobab dried fruit pulp as a novel food ingredient under Regulation (EC) No 258/97 of the European Parliament and of the Council. Official Journal of the European Union, 38–39.Google Scholar
  68. Thornthwaite, C. W. 1948. An approach toward a rational classification of climate. Geographic Review 38:55–94.CrossRefGoogle Scholar
  69. Thuiller, W. 2004. Patterns and uncertainties of species’ range shifts under climate change. Global Change Biology 10:2020–2027.CrossRefGoogle Scholar
  70. ———, B. Lafourcade, R. Engler, and M. B. Araújo. 2009. BIOMOD—A platform for ensemble forecasting of species distributions. Ecography 32:369–373.CrossRefGoogle Scholar
  71. Torras, M. 2000. The total economic value of Amazonian deforestation, 1978–1993. Ecological Economics 33:283–297.CrossRefGoogle Scholar
  72. Troy, A. and M. A. Wilson. 2007. Mapping ecosystem services: Practical challenges and opportunities in linking GIS and value transfer. Ecological Economics 60:852–853.CrossRefGoogle Scholar
  73. UNECA (United Nations Economic Commission for Africa). 2005. Striving for good governance in Africa: Synopsis of the African Government Report 2005.Google Scholar
  74. Vodouhê, F., O. Coulibaly, C. Greene, and B. Sinsin. 2009. Estimating the local value of non-timber forest products to Pendjari Biosphere Reserve dwellers in Benin. Economic Botany 63:397–412.CrossRefGoogle Scholar

Copyright information

© The New York Botanical Garden 2012

Authors and Affiliations

  • Jonathan Heubes
    • 1
    • 2
  • Katja Heubach
    • 1
    • 2
  • Marco Schmidt
    • 1
    • 2
    • 3
  • Rüdiger Wittig
    • 1
    • 2
  • Georg Zizka
    • 1
    • 2
    • 3
  • Ernst-August Nuppenau
    • 4
  • Karen Hahn
    • 1
    • 2
  1. 1.Biodiversity and Climate Research Centre (LOEWE BiK-F)Frankfurt/MainGermany
  2. 2.Institute of Ecology, Evolution and DiversityJ.W. Goethe-UniversityFrankfurt/MainGermany
  3. 3.Department of Botany and Molecular EvolutionSenckenberg Research InstituteFrankfurt/MainGermany
  4. 4.Agricultural and Environmental PolicyInstitute for Agricultural Policy and Market ResearchGießenGermany

Personalised recommendations